---
title: "Covid-19 in Egypt"
date:  "Last update: `r Sys.time()`"
author: Sherif Embarak^[https://github.com/Sherif-Embarak/]
output:
  flexdashboard::flex_dashboard:
    social: menu
    source: embed
    vertical_layout: scroll
    orientation: rows
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
rm(list=ls(all=TRUE))
options(stringsAsFactors = FALSE)
library(ggplot2)
library(plotly)
library(flexdashboard)
library(DT)
library(plotly)
library(knitr)
library(lubridate)
library(crosstalk)

setwd("D:/work/git corona/")
df <- read.csv("eg_covid.csv")
df$Day <- mdy(df$Day)
df$AccDay <- df$Day
df$Day <- paste(day(df$Day) , months.Date(df$Day) )
df$Curfew <- as.character(df$Curfew)
df$index <- as.numeric(rownames(df))
########################################################################
#weekly summary
AllDF <- df
shift <- 4
summary_AllDF <- data.frame(AllDF %>% mutate(week = ((as.numeric(AccDay)+shift) %/% 7) - ((as.numeric(min(AccDay))+shift) %/% 7)) %>% 
  group_by(week) %>%
  summarise(number_of_days= table(week), from = min(AccDay) , to = max(AccDay), sum(New.Cases) , sum(New.Deaths), sum(New.Recovered)))
summary_AllDF$number_of_days <- as.integer(summary_AllDF$number_of_days)
########################################################################
df <- df[,c(1:8,12)]
attach(df)
next_day <- max(index)+1
nd_new_case <-as.integer(exp(predict(glm(New.Cases~index ,family = gaussian(link = 'log') ),list(index=c(next_day)))))
nd_new_deaths <-as.integer(exp(predict(glm(New.Deaths+0.01~index ,family = gaussian(link = 'log') ),list(index=c(next_day)))))
nd_new_recoverd <-as.integer(exp(predict(glm(New.Recovered+0.01~index ,family = gaussian(link = 'log') ),list(index=c(next_day)))))
total_cases <- df$Total.Cases[nrow(df)]
total_deaths <- df$Total.Deaths[nrow(df)]
total_recovered <- df$Total.Recovered[nrow(df)]
closed <- total_deaths+total_recovered
active <- total_cases - closed
df$Day <- factor(df$Day, levels = df$Day)
xlabel <- df$Day[as.integer(seq(1 , nrow(df) , length.out = 10))]
hk <- highlight_key(df, ~Total.Cases)

chart1 <-ggplotly(ggplot(hk, aes(x=Day, y=New.Cases)) + scale_x_discrete(breaks = xlabel)+
  geom_bar(width = 0.3, stat = "identity")+scale_fill_manual(values = c("#6698FF", "#153E7E"))+
  theme(panel.grid.major.x  = element_blank(), axis.text.x = element_text(angle = 70, hjust = 1))+
    geom_smooth(aes(x=index, y=New.Cases), method = "glm", formula = y~x,method.args = list(family = gaussian(link = 'log')))+
    geom_text(label=paste("Tomorow's predication:",nd_new_case ), 
    x=10,
    y=max(New.Cases)
  )
  ,tooltip = c("x", "y")) %>%
       highlight(off = "plotly_relayout")

chart2 <-ggplotly(ggplot(hk, aes(x=Day, y=New.Deaths)) + scale_x_discrete(breaks = xlabel)+
  geom_bar(width = 0.3, stat = "identity")+scale_fill_manual(values = c("#6698FF", "#153E7E"))+
  theme(panel.grid.major.x  = element_blank(), axis.text.x = element_text(angle = 70, hjust = 1))+
    geom_smooth(aes(x=index, y=New.Deaths+0.01), method = "glm", formula = y~x,method.args = list(family = gaussian(link = 'log')))+
    geom_text(label=paste("Tomorow's predication:",nd_new_deaths ), 
    x=10,
    y=max(New.Deaths)
  )
  ,tooltip = c("x", "y")) %>%
       highlight(off = "plotly_relayout")

chart3 <-ggplotly(ggplot(hk, aes(x=Day, y=New.Recovered)) + scale_x_discrete(breaks = xlabel)+
  geom_bar(width = 0.3, stat = "identity")+scale_fill_manual(values = c("#6698FF", "#153E7E"))+
  theme(panel.grid.major.x  = element_blank(), axis.text.x = element_text(angle = 70, hjust = 1))+
    geom_smooth(data = hk, aes(x=index, y=New.Recovered+0.01), method = "glm", formula = y~x,method.args = list(family = gaussian(link = 'log')))+
    geom_text(label=paste("Tomorow's predication:",nd_new_recoverd ), 
    x=10,
    y=max(New.Recovered)
  )
  ,tooltip = c("x", "y")) %>%
       highlight(off = "plotly_relayout")

chart4 <- ggplotly(ggplot(data=hk, aes(x=Day, y=Total.Cases, group=1))+ scale_x_discrete(breaks = xlabel)+
 geom_line(color="#33CCFF", size=1)+theme(panel.grid.major.x  = element_blank(), axis.text.x = element_text(angle = 70, hjust = 1))+
  geom_point(color="#33CCFF"),tooltip = c("x", "y")) %>%
       highlight(off = "plotly_relayout")

chart5 <- ggplotly(ggplot(data=hk, aes(x=Day, y=Total.Deaths, group=1))+ scale_x_discrete(breaks = xlabel)+
 geom_line(color="#FF9900", size=1)+theme(panel.grid.major.x  = element_blank(), axis.text.x = element_text(angle = 70, hjust = 1))+
  geom_point(color="#FF9900"),tooltip = c("x", "y")) %>%
       highlight(off = "plotly_relayout")

chart6 <- ggplotly(ggplot(data=hk, aes(x=Day, y=Total.Recovered, group=1))+ scale_x_discrete(breaks = xlabel)+
 geom_line(color="#00DDDD", size=1)+theme(panel.grid.major.x  = element_blank(), axis.text.x = element_text(angle = 70, hjust = 1))+
  geom_point(color="#00DDDD"),tooltip = c("x", "y"))%>%
       highlight(off = "plotly_relayout")


```

Daily Dashboard
=======================================================================
This dashboard has been created according to  worldometers.info  design. In order to compensate with the lack of real time statistical data in Egypt, the given data below will be updated daily at 10:00 PM (Cairo Time).

Stay Home, Stay Safe.



Row
-----------------------------------------------------------------------

### Total Coronavirus Cases in Egypt

```{r, echo=FALSE}
valueBox(total_cases)
```

### Total Coronavirus Deaths in Egypt 

```{r, echo=FALSE}
valueBox(total_deaths, color="warning")
```

### Total Coronavirus Recovered Cases in Egypt

```{r, echo=FALSE}
valueBox(total_recovered , color = "#00DDDD")
```

### Cases which had an outcome `r "
"` `r paste0("Deaths/Discharged : ",   total_deaths, " (", round((total_deaths/closed)*100,1),"%)" )` `r "
"` `r paste0("Recovered/Discharged : ",   total_recovered, " (", round((total_recovered/closed)*100,1),"%)" )` 

```{r, echo=FALSE}
valueBox(paste("Closed Cases: ",closed))
```

### Currently Infected Patients

```{r, echo=FALSE}
valueBox(paste("Active Cases: ",active))
```

Row
-------------------------------------

### Distibution of Cases

```{r, echo=FALSE }
DT::datatable(hk,class = 'cell-border stripe hover compact', rownames = FALSE , options = list(pageLength = 1,order = list(2, 'desc')))%>%
  formatStyle('New.Cases', backgroundColor =  '#FFEEAA') %>% 
  formatStyle('New.Deaths',backgroundColor =  'red')%>%
  formatStyle(names(df),fontWeight = 'bold')%>%
  formatStyle('Curfew',  backgroundColor = styleEqual(c(0, 1), c('#a6cee3', '#1f78b4')))%>%
       highlight(on = "plotly_click" , off ="plotly_doubleclick")
```

Row
-------------------------------------

### Confirmed Cases per day
    
```{r, echo=FALSE ,fig.height=3}
chart1
```
    
### Deaths per day

```{r, echo=FALSE ,fig.height=3}
chart2
```

### Recovered Cases per day

```{r, echo=FALSE ,fig.height=3}
chart3
```

Row
-------------------------------------
    
### Total Confirmed Cases
    
```{r, echo=FALSE, warning=FALSE,message=FALSE,results='asis',fig.show='asis'}
chart4
```
    
### Total Deaths

```{r, echo=FALSE, warning=FALSE,message=FALSE,results='asis',fig.show='asis'}
chart5
```

### Total Recovered

```{r, echo=FALSE, warning=FALSE,message=FALSE,results='asis',fig.show='asis'}
chart6
```

   
Weekly Dashboard
=======================================================================

```{r, include=FALSE}
weekly_hk <- highlight_key(summary_AllDF, ~week)
chart7 <- ggplotly(ggplot(weekly_hk, aes(x=week , y=sum.New.Cases. , text =paste("from:", from, "to:",to))) +
  geom_bar(width = 0.4 , stat = "identity" , fill="steelblue")+
    geom_text(aes(label=sum.New.Cases.), color="black", size=3.5)+
  theme(panel.grid.major.x  = element_blank()),tooltip = c("x", "y" , "text")) %>%
       highlight(off = "plotly_relayout")

chart8 <-ggplotly(ggplot(weekly_hk, aes(x=week , y=sum.New.Deaths. , text =paste("from:", from, "to:",to))) +
  geom_bar(width = 0.4 , stat = "identity", fill="steelblue")+
    geom_text(aes(label=sum.New.Deaths.), color="black", size=3.5)+
  theme(panel.grid.major.x  = element_blank()),tooltip = c("x", "y" , "text")) %>%
       highlight(off = "plotly_relayout")

chart9 <-ggplotly(ggplot(weekly_hk, aes(x=week , y=sum.New.Recovered. , text =paste("from:", from, "to:",to))) +
  geom_bar(width = 0.4 , stat = "identity", fill="steelblue")+
    geom_text(aes(label=sum.New.Recovered.), color="black", size=3.5)+
  theme(panel.grid.major.x  = element_blank()),tooltip = c("x", "y" , "text")) %>%
       highlight(off = "plotly_relayout")
```

Row
-------------------------------------
    
### Chart 1
    
```{r, echo=FALSE , fig.height = 2, fig.width = 2}
DT::datatable(weekly_hk)%>%
       highlight(on = "plotly_click" , off ="plotly_doubleclick")
```

Row
-------------------------------------

### Chart 2
    
```{r, echo=FALSE}
cat("Weekly Distribution of New Confirmed Cases")
chart7
``` 
    
### Chart 3
    
```{r, echo=FALSE}
cat("Weekly Distribution of New Recovered Cases")
chart8
```
    
### Chart 4

```{r, echo=FALSE}
cat("Weekly Distribution of New Deaths")
chart9
```